Coffee ratings


This is my first Tidy Tuesday contribution and will be playing around a little bit with the the Coffee rating data .

Getting Tidy Tuesday data

data <-  tidytuesdayR::tt_load(2020, week = 28)
  
    Downloading file 1 of 1: `coffee_ratings.csv`
coffee <- data$coffee_ratings
head(coffee)

Coffee ratings distribution

p1 <- coffee %>% 
  drop_na(any_of("country_of_origin")) %>%
  filter(aroma != 0) %>% 
  ggplot() +
  aes(x = total_cup_points, fill = country_of_origin) +
  geom_density() +
  theme_minimal() +
  scale_fill_viridis_d(alpha = 0.7) +
  ylab("Proportion of coffes per country") +
  xlab("Total cup points") +
  xlim(60,100) +
  theme(
    plot.title = element_text(size = 18, face = "bold"),
    axis.title.x = element_text(size = 16),
    axis.title.y = element_text(size = 16),
    axis.text.y = element_text(size = 16),
    legend.text = element_text(size = 14),
    legend.title = element_blank(),
    legend.position = "bottom"
  ) 
  
p2 <- coffee %>% 
  drop_na(any_of("country_of_origin")) %>%
  filter(aroma != 0) %>% 
  ggplot() +
  aes(x = total_cup_points, y = country_of_origin, fill = country_of_origin) +
  geom_boxplot(show.legend = FALSE) +
  theme_minimal() +
  scale_fill_viridis_d(alpha = 0.7) +
  ylab("Countries") +
  xlab("Total cup points") +
  labs(fill = "Country") +
  
  theme(
    plot.title = element_text(size = 18, face = "bold"),
    axis.title.x = element_text(size = 16),
    axis.title.y = element_text(size = 16),
    axis.text.y = element_text(size = 18)
  ) +
  gghighlight(country_of_origin == "Colombia")


p2 / p1 + plot_annotation(tag_levels = 'A')

 



A work by Camilo Garcia